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                        Shantenu Jha                                                                              
209 Johnston Hall                                                                                 
Baton Rouge, LA 70803                                                                       

Ph: +1 225 578 8772                                                                        
sjha@cct.lsu.edu

Education:
2004 Ph.D.,  Computational Physics,  Syracuse  University
2001 M.S.,   Computer Science, Syracuse University,
1995 M.Sc,  Physics, IIT - Delhi
                                                         

I am Senior Research Scientist and lead of the Computational Biology Research Group at the CCT. I am also a Research Assistant Professor in the Computer Science Department at LSU. Before this I was a Research Fellow on the RealityGrid project at the Centre for Computational Science, UCL.


My research interests are primarily in Computational Physics and Grid Computing. My working definition of the later is that it is a novel, potentially very useful, though currently very painful way of doing the former. When I'm not busy feeling the pain (e.g. battling "lower upper-level grid-middleware" or put simply -- grid software) or with work that aims to lessen the pain (grid programming models, "Simple" APIs for grid applications), I work on research problems in Statistical Physics. For the past few years I've been working on biologically inspired problems using computational techniques (primarily molecular dynamics simulations) and statistical physics tools. I've also looked at the transport properties of electrons in Quantum Dots using Monte Carlo simulations. I won't embarrass my collaborators by mentioning them here. I try not to stop them from doing all the work; they try to stop me from taking all the glory. Together that keeps us all pretty busy...

My research can be summed up in the following sentence: I strive to find ways to be lazy. One of the first things I learnt from my Ph.D advisor is that  -- "It takes a lot of effort to be Lazy". What that really means is that as computational scientists, we're always trying to find quicker, better and easier ways to solve problems numerically. One way  computational scientists can sustain such "laziness" is to use  new, sophisticated algorithms -- derived from numerical as well as physical insight -- that solve scientific problems quicker. Experience has taught us however, that often that isn't  enough. New algorithms can be very difficult to come by. And although some algorithms present significant advances theoretically, if implemented using traditional computing approaches they may not be any better than the rest in practice. I've come to believe that the best way forward is an approach that permits the flexibility of new computing techniques to be used with algorithmic advances. We have tested many  of these postulates in the joint UK-US project (SPICE), for which I'm the scientific lead. It aims to use grid computing techniques to understand the translocation of biomolecules in protein nanopores in ways that haven't been possible before. Click for a glimpse of some of the  reviews and awards this work has won.

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